Some attempts have been made as to automated apparel silhouette recognition. Past approaches have included attempts at a simple clothing searching method that extracts shape context features on segmentation boundary points to recognize a few salient clothing silhouette attributes, such as sleeveless, v-neck and so on. Despite the closeness of the targeting problem, the past approaches did not perform well. Additionally, learning based clothing attribute recognition methods were proposed as part of a multimedia fitting mirror system. In these approaches, various specific image features, including skin area, the distribution of Harris corner points and Canny edge points, were extracted and fed into Support Vector Machines to learn attribute classifiers. Harris corner points refer to a technique that identifies specific regions of an image where the brightness of an image is deemed to shift dramatically along X and Y axes. Canny edge points refers to a technique that uses a brightness gradient to detect an edge within an image. The proposed methods were reported to exhibit 75%-90% accuracy in test environments as to recognizing features such as sleeve length, collar existence, placket length. Another branch of related work is automatic attribute discovery, which are focused on identifying potential attribute phrases that can be consistently described by some aspect of an object's visual appearance. A previous approach discovered attributes such as “front platform”, “high heel”, “clogs” in approximately 37795 images collected from a shopping website. The approach also categorized attributes according to their localizability and visual feature type.